Monitoring of Soil Moisture Content of Winter Wheat Based on Hyperspectral and Machine Learning Models

被引：0
作者：
Tang Z. [1 ,2 ]
Zhang W. [1 ,2 ]
Xiang Y. [1 ,2 ]
Li Z. [1 ,2 ]
Zhang F. [1 ,2 ]
Chen J. [1 ,2 ]
机构：
[1] Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Northwest a and F University, Ministry of Education, Shaanxi, Yangling
[2] Institute of Water-saving Agriculture in Arid Areas of China, Northwest A and F University, Shaanxi, Yangling
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2023年 / 54卷 / 12期
关键词：
hyperspectral; machine learning; soil moisture content; winter wheat;
D O I：
10.6041/j.issn.1000-1298.2023.12.034
中图分类号：
学科分类号：
摘要：
Aiming to promptly obtain soil moisture content (SMC) in the root zone of field crops for precise irrigation, hyperspectral technology was utilized. Over a 2-year period spanning from 2019 to 2020 , during the winter wheat jointing stage, SMC data at varying soil depths and hyperspectral data were collected. Three categories of vegetation indices were created, comprising trilateral' spectral parameters related to blue, yellow, and red-edge areas, any two-band vegetation indices with the highest correlation to winter wheat root zone SMC , and empirical vegetation indices showing good correlation with crop parameters from previous studies. The vegetation indices exhibited the highest correlation with SMC at different soil depths were selected. Subsequently, random forest (RF) , back propagation neural network (BPNN) , and extreme learning machine (ELM) were employed to construct SMC estimation models, using the selected vegetation indices as model inputs. The results revealed that a majority of the trilateral ' spectral parameters spectral indices, any two-band vegetation indices, and empirical vegetation indices displayed stronger correlations with SMC in the 0 ~ 20 cm soil layer in comparison with the 20 ~ 40 cm and 40 ~ 60 cm layers. The two-band combinations in the 0 ~ 20 cm layer exhibited the highest correlations with SMC, all surpassing 0. 8. Among which, RI showed the highest correlation with SMC at 0. 851 , utilizing a wavelength combination of 675 nm and 695 nm. The RF model emerged as the most effective modeling method for SMC , with the highest accuracy observed in the 0 ~ 20 cm soil layer. The coefficient of determination ( R ) for the validation set of the estimation model in the 0 ~ 20 cm layer reached 0. 909 , and the root mean square error ( RMSE ) was 0. 008 , while the mean relative error ( MRE) was 3.949%. The outcomes can serve as a foundation for hyperspectral monitoring of winter wheat root zone SMC and provide valuable insights for the rapid assessment of crop growth under water stress. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.
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页码：350 / 358
页数：8
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